Opportunity identification and forecasting for search engine optimization

ABSTRACT

A method of optimizing placement of references to an entity includes identifying at least search term to be optimized, determining a score for results of a search of a network with respect to the entity, determining costs associated with improving the score, and determining values associated with improving the score.

BACKGROUND OF THE INVENTION The Field of the Invention

The Internet has changed the way people gather information, establishrelationships with one another and even how people communicate with oneanother. Additionally, the Internet has changed the way companies seekpotential customers and even what the meaning of a business is. It haschanged the way companies advertise, sell, coordinate with one anotherand compete with one another. With this change has come a huge explosionin the number of Web Pages for people to visit. Search engines, such asGoogle, Bing, Yahoo and others have come into being to help people findtheir way to Web Pages that they desire. As a result, the number andtypes of channels that a marketer can leverage has also exploded—beyondorganic and paid search, they can also leverage blogs, social media,video sharing, mobile content and ads, display ads, and many otherchannels.

Additionally, tracking the behavior of the actions of each visitor wouldallow the Web Page to be marketed more efficiently. In particular, manyWeb Pages track their organic search performance in search engines basedon number of visits for certain keywords. However, they cannot determinehow many visitors came as a result of a particular search engine resultand rank position to the Web Page, instead they must estimate this basedon the data (referral header) passed to the web page which only helpsthem determine the number of visitors that came from a specific keyword.Without understanding key attributes of their performance on the searchengine, they cannot accurately determine the effectiveness of theirmarketing efforts. Moreover, they cannot determine how their organicsearch marketing efforts would impact what those visitors do on the WebPage when they have found the Web Page. For example, if a Web Page isselling merchandise, there is currently no way to determine whocompleted a particular purchase on the Web Page and compare that withhow that visitor came to the Web Page.

Therefore, owners and designers of Web Pages must estimate how visitorshave come to the Web Page and what they do once they are on the WebPage. This does not allow them to determine which actions would presenta better chance for success of the Web Page. For example, a Web Pageowner might be confronted with limited marketing budgets that allow themto either improve their ranking in search engine results or that willplace advertisements for their Web Page on other Web Pages but not both.Currently, the Web Page owner must choose which strategy to follow withlimited information on which would be more effective.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one exemplary technology area where some embodimentsdescribed herein may be practiced.

BRIEF SUMMARY OF THE INVENTION

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential characteristics of the claimed subject matter, nor is itintended to be used as an aid in determining the scope of the claimedsubject matter.

A method of optimizing placement of references to an entity may includeidentifying at least search term to be optimized, determining a scorefor results of a search of a network with respect to the entity,determining costs associated with improving the score, and determiningvalues associated with improving the score.

In another example, method for optimizing online references to an entitymay include searching at least one channel on a network for referencesto the entity using a plurality of search terms to generate searchresults. The references associated with each of the plurality of searchterms may be scored to generate scores for the references within thesearch results with respect to the entity. Conversions by one or morevisits the entity with the search terms that directed the visits to theentity to determine a conversion rate may also be correlated. The methodmay also include determining a total value of the visits to the entityand displaying the search terms, the scores for the references withinthe search results with respect to the entity, the visits, theconversion rate and the total value.

In yet another example, a system for optimizing online references to anentity may include a correlator configured to determine internal datafor search terms associated with the references, a deep index engineconfigured to generate scores for the references within the searchresults with respect to the entity, and a forecasting engine configuredto correlate internal data with the scores for the references.

These and other objects and features of the present invention willbecome more fully apparent from the following description and appendedclaims, or may be learned by the practice of the invention as set forthhereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

To further clarify various aspects of some example embodiments of thepresent invention, a more particular description of the invention willbe rendered by reference to specific embodiments thereof which areillustrated in the appended drawings. It is appreciated that thesedrawings depict only illustrated embodiments of the invention and aretherefore not to be considered limiting of its scope. The invention willbe described and explained with additional specificity and detailthrough the use of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of a system for optimizing placementof references to an entity;

FIG. 2 illustrates a flowchart of an exemplary method of optimizingplacement of references to an entity;

FIGS. 3 and 4 illustrate an exemplary method for identifyingopportunities;

FIGS. 5 and 6 illustrate an exemplary method for forecasting results aninitiative; and

FIG. 7 illustrates a chart for tracking results of an initiative.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Systems and methods are provided herein for combining data from internalsources (e.g. internal web analytics data, web server logs, and thelike) with third party data (e.g., search engine data provided by searchengines such as the CPC value of a keyword, the search frequency of akeyword) and external data (e.g., data crawled on external web pages).Using the combined data, the system may mine for trends and/or conductautomated analysis to surface opportunities (e.g., finds keywords thatare spiking in search volume, that the customer does not rank on and hasa good opportunity to rank on because the competition is weak).

Systems and method are also provided to identify trends frominternal/third party/external data in order to see where opportunitiesare (e.g., what are keywords that are spiking in search volume, what arekeywords that my competition does not rank on, how are users changingtheir search behavior).

Alternatively or additionally, systems and methods may be providedherein to assign values to the data (e.g., what is the value of akeyword) based on automated value algorithms, values as some form ofcustom formula defined by user, apply probabilistic modeling to the datafor the purpose of forecasting.

FIG. 1 illustrates a block diagram of a system 100 for optimizingplacement of references to an entity within one or more channels.Entities can include individuals, corporations, brands, products, modelsor any other entities referenced anywhere on a network such as theInternet. The references may include links and/or references to one ormore Web Pages or other media, such as display advertisements,associated with the entity. Accordingly, the references can includeorganic references, online advertisements including displayadvertisements, news items or any other reference to the entity.

FIG. 1 shows that the system 100 can include a network 105. In at leastone implementation, the network 105 can be used to connect the variousparts of the system 100 to one another, such as between a webserver 110,a deep index engine 120, a correlator 130, and a forecasting engine 140.It will be appreciated that while these components are being shown asseparate, the components may be combined as desired. Further, while oneof each component is illustrated, it will be appreciated that the system100 may include any number of each of the components shown.

As will be discussed in more detail hereinafter, the forecasting engine140 is configured to determine a search term or search terms tooptimize. The search term or terms may be selected from a group orbasket of known search terms that may affect actions related to theentity. The forecasting engine 140 may also be configured to helpmarketers forecast the business value of optimization initiatives (e.g.,if I work on optimizing for a given 5 keywords, what is the likelyresult of improvement in my search engine rank position and how muchmore incremental revenue will be generated from the improvement) andalso take into account the difficulty and expense associated with theinitiative.

In at least one example, the network 105 includes the Internet,including a global internetwork formed by logical and physicalconnections between multiple wide area networks and/or local areanetworks and can optionally include the World Wide Web (“Web”),including a system of interlinked hypertext documents accessed via theInternet. Alternately or additionally, the network 105 includes one ormore cellular RF networks and/or one or more wired and/or wirelessnetworks such as, but not limited to, 802.xx networks, Bluetooth accesspoints, wireless access points, IP-based networks, or the like. Thenetwork 105 can also include servers that enable one type of network tointerface with another type of network.

In at least one implementation, the web server 110 (or “webserver”) caninclude any system capable of storing and transmitting a Web Page to auser. For example, the web server 110 can include a computer programthat is responsible for accepting requests from clients (user agentssuch as web browsers), and serving them HTTP responses along withoptional data contents, which can include HTML documents and linkedobjects for display to the user. Additionally or alternatively, the webserver 110 can include the capability of logging some detailedinformation, about client requests and server responses, to log files.

The entity can include any number of Web Pages. The aggregation ofreferences to the various Web Pages can be referred to as traffic. Itshould be noted that “Web Page” as used herein refers to any onlineposting, including domains, subdomains, Web posts, Uniform ResourceIdentifiers (“URIs”), Uniform Resource Locators (“URLs”), images,videos, or other piece of content and non-permanent postings such ase-mail and chat unless otherwise specified.

In at least one implementation, external references to a Web Page caninclude any reference to the Web Page which directs a visitor to the WebPage. For example, an external reference can include text documents,such as blogs, news items, customer reviews, e-mails or any other textdocument which discusses the Web Page. Additionally or alternatively, anexternal reference can include a Web Page which includes a link to theWeb Page. For example, an external reference can include other WebPages, search engine results pages, advertisements or the like.

In the illustrated example, the deep index engine 120 is configured touse search terms identified above to perform a search of the network toidentify references to the entity. The deep index engine 120 is furtherconfigured to score the results of the search of the network withrespect to the entity. This score may include a position at whichreferences to the entity are displayed within the search results. Therelative position of the references to the entity within the searchresult can affect how the references affect actions related to theentity. Accordingly, by determining the relative position of thereferences within search results, the deep index engine 120 is able todetermine a current performance metric for each of the search terms asthey relate to the entity.

Additionally or alternatively, the deep index engine 120 may beconfigured to score the search results for each of the search terms withrespect to other entities, including entities found in the competitivelisting for the search results. Accordingly, the deep index engine 120may be configured to gather external data related to performance ofother entities to establish current baselines for those entities aswell.

Additionally or alternatively, the deep index engine 120 may be furtherconfigured to crawl the search results related to each of the searchterms to retrieve external data. In particular, the deep index enginemay be configured to crawl the search results for each of the searchterms and analyze data associated with the crawl, including on-pageinformation and back link data (e.g back link URL, anchor text, etc) foreach URL in the search result. The deep index engine 120 may thenanalyze the data to identify additional search terms that may berelevant to the entity, but which may not have been searched or on whichthe entity does not rank. In at least one example, this analysis mayinclude conducting a keyword frequency search. Accordingly, the deepindex engine 120 may be configured to surface additional search terms.In at least one example, these additional search terms and opportunitiesidentified and targeted in any channel (SEO, paid search, socialnetworks, etc.) Cross-channel opportunities are also a part of theopportunity identification (e.g. if a customer is not ranking on akeyword on organic search that a competitor ranks on, the customer canimmediately target this keyword in paid search.)

An exemplary deep index engine is described in more detail in copendingU.S. patent application Ser. No. 12/436,704 entitled “COLLECTING ANDSCORING ONLINE REFERENCES” filed May 6, 2009, the disclosure of which ishereby incorporated by reference in its entirety.

Additional current performance metrics may include internal datadetermined by the correlator 130. In at least one implementation, thecorrelator 130 can determine how visitors are directed to the entity andhow those visitors behave once there. For example, the correlator 130can correlate conversion of visits to the search terms that drove thevisits.

An exemplary correlator is described in more detail in co-pending U.S.patent application Ser. No. 12/574,069 filed Oct. 6, 2009 and entitled“CORRELATING WEB PAGE VISITS AND CONVERSIONS WITH EXTERNAL REFERENCES”the disclosure of which is hereby incorporated by reference in itsentirety.

As will be discussed in more detail hereinafter, the forecasting engine140 may receive data from third parties including information aboutnetwork activity related to the search terms described above. Theforecasting engine 140 may also be configured to receive the internaldata, including the output of the correlator 130 as well as externaldata, including the output of the deep index engine 120. The forecastingengine 140 may use the internal data, the third party data, and theexternal data to identify opportunities for optimizing placement ofreferences to the entity as well as to forecasting the likely costs andbenefits of improving references to the entity.

FIG. 2 illustrates a flowchart of an exemplary method of optimizingplacement of references to an entity. The method can be implementedusing software, hardware or any combination thereof. If the method isimplemented using hardware, the steps of the method can be stored in acomputer-readable medium, to be accessed as needed to perform theirfunctions. Additionally, if the method is implemented using software,the steps can be carried out by a processor, field-programmable gatearray (FPGA) or any other logic device capable of carrying out softwareinstructions or other logic functions.

Additionally or alternatively, the method can be implemented using aserver or other single computing environment. If a server or othersingle computing environment is utilized, the conversions need not bedivided into groups, since all conversions will be analyzed by the sameserver or single computing environment.

FIG. 2 illustrates a method of optimizing placement of references to anentity within one or more channels. As illustrated in FIG. 2, the methodbegins at step 200 by determining search terms. In at least one example,search terms may include keywords retrieved from a keyword database. Thekeyword database contains one or more keywords to be used in the pagesearch. In some embodiments, additional search terms may be surfaced bycrawling previous search results, as introduced above.

At step 210, internal data is retrieved related to the search terms. Forexample, previous actions related to the network to determine a totalnumber of conversions associated with the search terms as well as thetotal value of those conversions. This internal data may be retrieved orcalculated in any desired manner.

The method also includes at step 220 receiving third party data relatedto the search terms. This third party data may include any desiredinformation, including information about network activity related to thesearch terms. For example, third party data may include, withoutlimitation, search engine data such as cost per click (CPC) values forthe search terms, search frequency for the keywords, and any otherdesired data that may be provided by third parties. Requests for and/orreceipt of third party data may take place at any point, includingsimultaneous retrieving internal data related to the search terms atstep 210.

Still referring to FIG. 2, the method also includes at step 230performing a search in which the search terms are used to search thenetwork for references to the entity. Any method may be used to searchthe network for references to the entity. Further, any number ofchannels within the network may be searched as desired. In at least oneexample, performing the search may include scoring the results of thesearch of the network with respect to the entity. This score may includea position at which references to the entity are displayed within thesearch results.

Performing the search may also include performing a crawl of the searchresults related to each of the search terms. In particular, the methodmay include crawling the search results for each of the search terms andanalyzing data associated with the crawl, including on-page informationand back link data (e.g. back link URL, anchor text, etc) for each URLin the search result.

At step 240, the results of one or more of steps 200-210 may then beanalyzed to identify opportunities and to forecast results ofinitiatives at step 250. An exemplary method for identifyingopportunities will be discussed with reference to FIGS. 3 and 4 and anexemplary method for forecasting will be discussed with reference toFIGS. 5 and 6.

As illustrated in FIG. 3, a method for identifying opportunities tooptimize references may begin by correlating internal data and externaldata. Optionally, third party data may also be included in thecorrelation. In at least one example correlating internal and externaldata includes correlating scores for each of the search terms withrespect to references to the entity, the total number of visits relatedto the network associated with each search term, the number ofconversions associated with those visits, the ratio of conversions tovisits, and the total value of the conversions associated with thesearch terms.

Correlating these variables may bring into focus he search terms scorewith respect to the entity and how that score eventually results invalue to the entity. Accordingly, at step 310 the method may includedisplaying search results to a user. An example of such a display isillustrated in FIG. 4.

Referring again to FIG. 3, once the internal and external data have beencorrelated, search terms may be identified for investigation as at step320. Identifying search terms for investigation may include identifyingsearch terms for which the references score poorly with respect to theentity. Such an example may include which scores place the references ona second page or worse on search results.

Conversion rates and/or total values may then be analyzed to determinewhether the search terms are worth investigating. For example, if thesearch terms have a high conversion rate, it may be worth investigatingimproving the score for those search terms with respect to the entity.Further, if the total value associated with search term is relativelylarge despite a poor score, this may indicate that improving the scoreof the search term may be worth investigating. Accordingly, a method foridentifying search terms for investigation may include determining ascore threshold, such as a page rank score, determining a thresholdconversion ratio and determining a threshold total value. If theparameters associated with a score are met and either or both of theconversion threshold or value threshold are met, the search term mayautomatically be identified for investigation.

Additionally or alternatively, the external data described above may beanalyzed to score search results for references to another entity, suchas a competitor. The scores associated with the search terms may then beanalyzed to determine where another competitor may be weak. For example,if a competitor ranks low on a search term that has significant trafficor visits associated therewith as reflected in the third party data,that search term may be identified for further investigation.

Additionally or alternatively, the external data analyzed to scoresearch results for references to another may indicate where the entityis weak. For example, additional search may be identified by crawlingsearch results for a given set of search terms, as described above. Theadditional search terms may then be searched and a score generated forthe search results with respect to both the entity and to competitors.If the scores indicate that the competitors score well with respect tothose search terms and the entity does not, that determination mayindicate the search terms are worth investigating, such as by targetingthe search terms in paid searches. In at least one example, a thresholdrank may be determined for the entity, such as a rank that indicatesthat references to the entity are appearing on a third page or worse.Any threshold rank may be used as desired. In such an example, if acompetitor scores better than the threshold rank with respect to thesearch terms and the entity scores worse than the threshold rank, thesearch terms may be automatically target for a paid search.

Additionally or alternatively, third party data may indicate thatactivity related to certain search parameters has spiked. This spikeitself may identify the search terms as being worth investigating.

FIG. 5 illustrates a method for forecasting results of an initiativeaccording to one example. The search terms may be generated by a user,may be surfaced according to the method for identifying opportunitiesdescribed above, or by some other method. As a preliminary step, thesearch terms or other variables associated with the initiative may beanalyzed as described with reference to FIGS. 2-3. Thereafter, asillustrated in FIG. 5, the method may begin by determining the currentparameters associated with the search terms for actions related to theentity. These parameters may include the internal and external data,such as correlated scores for each of the search terms with respect toreferences to the entity, the total number of visits related to thenetwork associated with each search term, the number of conversionsassociated with those visits, the ratio of conversions to visits, andthe total value of the conversions associated with the search terms.These parameters may also include third party data.

Once the current parameters for the search terms are determined, at step510 the method estimates the increase in actions associated withimproving the scores for the search terms with respect to the entity.These estimates may be made a probabilistic model using data obtainedfrom any of the sources described above. For example, it may beunderstood that keywords at given positions receive a relativelypredictable percentage of the network traffic or visits for that page.

At step 520, the method continues with determining a cost for improvingscores. For example, improving scores may include building back links tothe entity. Determining a cost of improving scores may include trackingprevious increases of back links and correlating previous improvementsin rank. A historical regression analysis or other methodology may thenbe applied to the previous efforts to estimate a cost for improvingscores based on the cost and time associated with activities thatimprove the score.

At step 530, the method continues with determining a value for improvingscores using any desired calculation, such as user-defined formulas,probabilistic modeling or any other method. Accordingly, the presentmethod allows marketers or other users to forecast likely outcomes forinitiatives.

FIG. 6 illustrates a chart 600 that may be generated aid marketers indetermining values of search terms. As illustrated in FIG. 6, the chartmay plot rank position on a search engine against keyword search volume.A number of “bubbles” 610 represent various search terms. Each bubble610 may represent a search term or group of search terms. The bubblesmay also be color coded as desired to indentify which entity isreferenced. Diameters of the bubbles may represent conversion rates orother desired variables for the search terms.

FIG. 7 illustrates a chart showing how selected parameters may betracked over time, including those described above. Such chart canprovide a useful tool in tracking the progress of initiatives, such asthose described above.

Embodiments within the scope of the present invention also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures and which can be accessed by a generalpurpose or special purpose computer. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to acomputer, the computer properly views the connection as acomputer-readable medium. Thus, any such connection is properly termed acomputer-readable medium. Combinations of the above should also beincluded within the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Although the subject matter has been described inlanguage specific to structural features and/or methodological acts, itis to be understood that the subject matter defined in the appendedclaims is not necessarily limited to the specific features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example forms of implementing the claims.

As used herein, the term “module” or “component” can refer to softwareobjects or routines that execute on the computing system. The differentcomponents, modules, engines, and services described herein may beimplemented as objects or processes that execute on the computing system(e.g., as separate threads). While the system and methods describedherein are preferably implemented in software, implementations inhardware or a combination of software and hardware are also possible andcontemplated. In this description, a “computing entity” may be anycomputing system as previously defined herein, or any module orcombination of modulates running on a computing system.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A method for optimizing online references to anentity that are non-paid advertisements, the method comprising:searching at least one channel unassociated with paid advertisements ona network for references to the entity unassociated with paidadvertisements using a plurality of search terms to generate searchresults that include a plurality of references; scoring the referencesto the entity associated with each of the plurality of search terms fromthe plurality of references to generate scores for the references to theentity; correlating conversions by one or more visits to a website ofthe entity through the reference with the search terms that directed thevisits to the entity to determine a conversion rate; determining a totalvalue of the conversions to the entity; correlating at least the totalvalue of the conversions to the entity associated with the references tothe entity and the scores for the references to the entity to identifyone or more of the plurality of search terms; and for the identified oneor more of the plurality of search terms, forecasting an increase inconversions for the references to the entity associated with an increasein the scores for the references to the entity.
 2. The method of claim1, wherein searching the at least one channel includes searching atleast one of: organic searches, page searches, e-mail, blogs, socialnetworks, social news, affiliate marketing, discussion forums, newssites, rich media, and social bookmarks.
 3. The method of claim 1,wherein using a plurality of search terms to generate search resultsincludes using a plurality of keywords.
 4. The method of claim 3,wherein using a plurality of keywords further includes crawlingpreviously returned search results and conducting a keyword frequencyanalysis to identify at least some of the plurality of keywords.
 5. Themethod of claim 1, wherein scoring the references to the entityassociated with each of the plurality of search terms includesdetermining a keyword rank.
 6. The method of claim 1, further comprisingscoring references unassociated with the entity and associated with eachof the plurality of search terms to generate scores for the referencesunassociated with the entity within the search results with respect tocompetitive listings; comparing the scores of the references to theentity with the scores for the references unassociated with the entitywith respect to competitive listings; and displaying the search terms,the competitive listings, and the scores for the references unassociatedwith the entity with respect to the competitive listings.
 7. The methodof claim 1, further comprising determining costs for improving thescores of the references to the entity.
 8. The method of claim 7,further comprising determining values for improving the scores of thereferences to the entity associated with the search terms and selectingreferences to be improved based on determining the costs and values forimproving the scores of the references to the entity associated with thesearch terms.
 9. The method of claim 8, further comprising optimizingthe scores of the references to the entity based on the steps ofdetermining the costs and values for improving the scores of thereferences associated with the search terms.
 10. The method of claim 1,further comprising: crawling the plurality of search results todetermine additional search terms; searching the at least one channelunassociated with paid advertisements on the network using theadditional search terms to generate additional search results;determining scores for references to the entity and for references to anadditional entity included in the additional search results; analyzingthe scores for the references to the entity and for the references tothe additional entity to determine if the entity ranks with respect tothe additional search terms and if the additional entity ranks withrespect to the additional search terms; and automatically targeting thesearch terms in a paid search if the additional entity ranks above afirst threshold score and the entity ranks below a second thresholdscore.
 11. A non-transitory computer readable storage medium configuredto cause a system to perform operations of optimizing online referencesto an entity that are non-paid advertisements, the operationscomprising: searching at least one channel unassociated with paidadvertisements on a network for references to the entity unassociatedwith paid advertisements using a plurality of search terms to generatesearch results that include a plurality of references; scoring thereferences to the entity associated with each of the plurality of searchterms from the plurality of references to generate scores for thereferences to the entity; correlating conversions by one or more visitsto a website of the entity through the reference with the search termsthat directed the visits to the entity to determine a conversion rate;determining a total value of the conversions to the entity; correlatingat least the total value of the conversions to the entity associatedwith the references to the entity and the scores for the references tothe entity to identify one or more of the plurality of search terms; andfor the identified one or more of the plurality of search terms,forecasting an increase in conversions for the references to the entityassociated with an increase in the scores for the references to theentity.
 12. The non-transitory computer readable storage medium of claim11, wherein the operations further comprise scoring the references tothe entity associated with each of the plurality of search termsincludes determining a keyword rank.
 13. The non-transitory computerreadable storage medium of claim 11, wherein the operations furthercomprise: scoring references unassociated with the entity and associatedwith each of the plurality of search terms to generate scores for thereferences unassociated with the entity within the search results withrespect to competitive listings; comparing the scores of the referencesto the entity with the scores for the references unassociated with theentity with respect to competitive listings; and displaying the searchterms, the competitive listings, and the scores for the referencesunassociated with the entity with respect to the competitive listings.14. The non-transitory computer readable storage medium of claim 11,wherein the operations further comprise determining costs for improvingthe scores of the references to the entity.
 15. The non-transitorycomputer readable storage medium of claim 14, wherein the operationsfurther comprise determining values for improving the scores of thereferences to the entity associated with the search terms and selectingreferences to be improved based on determining the costs and values forimproving the scores of the references to the entity associated with thesearch terms.